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      Machine learning models for predicting depression in Korean young employees

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          Abstract

          Background

          The incidence of depression among employees has gradually risen. Previous studies have focused on predicting the risk of depression, but most studies were conducted using basic statistical methods. This study used machine learning algorithms to build models that detect and identify the important factors associated with depression in the workplace.

          Methods

          A total of 503 employees completed an online survey that included questionnaires on general characteristics, physical health, job-related factors, psychosocial protective, and risk factors in the workplace. The dataset contained 27 predictor variables and one dependent variable which referred to the status of employees (normal or at the risk of depression). The prediction accuracy of three machine learning models using sparse logistic regression, support vector machine, and random forest was compared with the accuracy, precision, sensitivity, specificity, and AUC. Additionally, the important factors identified via sparse logistic regression and random forest.

          Results

          All machine learning models demonstrated similar results, with the lowest accuracy obtained from sparse logistic regression and support vector machine (86.8%) and the highest accuracy from random forest (88.7%). The important factors identified in this study were gender, physical health, job, psychosocial protective factors, and psychosocial risk and protective factors in the workplace.

          Discussion

          The results of this study indicated the potential of machine learning models to accurately predict the risk of depression among employees. The identified factors that influence the risk of depression can contribute to the development of intelligent mental healthcare systems that can detect early signs of depressive symptoms in the workplace.

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          Most cited references72

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          The CES-D Scale: A Self-Report Depression Scale for Research in the General Population

          L Radloff (1977)
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            The Satisfaction With Life Scale.

            Ed Diener, Robert A Emmons, Randy Larsen … (2010)
            This article reports the development and validation of a scale to measure global life satisfaction, the Satisfaction With Life Scale (SWLS). Among the various components of subjective well-being, the SWLS is narrowly focused to assess global life satisfaction and does not tap related constructs such as positive affect or loneliness. The SWLS is shown to have favorable psychometric properties, including high internal consistency and high temporal reliability. Scores on the SWLS correlate moderately to highly with other measures of subjective well-being, and correlate predictably with specific personality characteristics. It is noted that the SWLS is Suited for use with different age groups, and other potential uses of the scale are discussed.
            Article 0 comments Cited 3801 times – based on 0 reviews     Review now
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              A very brief measure of the Big-Five personality domains

              Samuel D. Gosling, Peter Rentfrow, William Swann (2003)
              Article 0 comments Cited 1072 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Public Health
                Journal ID (iso-abbrev): Front Public Health
                Journal ID (publisher-id): Front. Public Health
                Title: Frontiers in Public Health
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 2296-2565
                Publication date (Electronic): 12 July 2023
                Publication date Collection: 2023
                Volume: 11
                Electronic Location Identifier: 1201054
                Affiliations
                [1] 1College of Nursing, Ewha Womans University , Seoul, Republic of Korea
                [2] 2Department of Medical Life Sciences, School of Medicine, The Catholic University of Korea , Seoul, South Korea
                Author notes

                Edited by: Harshavardhan Sampath, Sikkim Manipal University, India

                Reviewed by: Seyed-Ali Sadegh-Zadeh, Staffordshire University, United Kingdom; Delia Virga, West University of Timișoara, Romania

                *Correspondence: Minji Gil, zoemjgil@ 123456gmail.com
                Article
                DOI: 10.3389/fpubh.2023.1201054
                PMC ID: 10371256
                PubMed ID: 37501944
                SO-VID: 7fc12d78-eea3-4b0d-8711-8f81bd1f4e1f
                Copyright © Copyright © 2023 Kim, Gil and Min.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 06 April 2023
                Date accepted : 12 June 2023
                Page count
                Figures: 1, Tables: 4, Equations: 0, References: 75, Pages: 10, Words: 6739
                Funding
                Funded by: Ministry of Science, ICT and Future Planning, doi 10.13039/501100003621;
                Award ID: NRF 2022R1A2C2004867
                Award ID: 2022R1A6A3A01086554
                Award ID: 2021R1F1A1058613
                Funded by: National Research Foundation of Korea, doi 10.13039/501100003725;
                Categories
                Subject: Public Health
                Subject: Original Research
                Custom metadata
                section-at-acceptance Public Mental Health

                Keywords: machine learning,depression,employees,workplace,prediction
                Data availability:
                Keywords: machine learning, depression, employees, workplace, prediction

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